Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA.
Molecular Health GmbH, Heidelberg, Germany.
CPT Pharmacometrics Syst Pharmacol. 2018 Dec;7(12):809-817. doi: 10.1002/psp4.12356. Epub 2018 Oct 24.
Clinical trials can fail to detect rare adverse events (AEs). We assessed the ability of pharmacological target adverse-event (TAE) profiles to predict AEs on US Food and Drug Administration (FDA) drug labels at least 4 years after approval. TAE profiles were generated by aggregating AEs from the FDA adverse event reporting system (FAERS) reports and the FDA drug labels for drugs that hit a common target. A genetic algorithm (GA) was used to choose the adverse event (AE) case count (N), disproportionality score in FAERS (proportional reporting ratio (PRR)), and percent of comparator drug labels with an AE to maximize F-measure. With FAERS data alone, precision, recall, and specificity were 0.57, 0.78, and 0.61, respectively. After including FDA drug label data, precision, recall, and specificity improved to 0.67, 0.81, and 0.71, respectively. Eighteen of 23 (78%) postmarket label changes were identified correctly. TAE analysis shows promise as a method to predict AEs at the time of drug approval.
临床试验可能无法检测到罕见的不良事件 (AEs)。我们评估了药物靶标不良事件 (TAE) 谱在药物批准后至少 4 年后预测美国食品和药物管理局 (FDA) 药物标签上不良事件的能力。TAE 谱是通过汇总来自 FDA 不良事件报告系统 (FAERS) 报告和 FDA 药物标签的药物不良事件而生成的，这些药物针对共同的靶点。遗传算法 (GA) 用于选择不良事件 (AE) 病例数 (N)、FAERS 中的比例失调评分 (比例报告比 (PRR)) 和具有 AE 的比较药物标签的百分比，以最大化 F 度量。仅使用 FAERS 数据时，精度、召回率和特异性分别为 0.57、0.78 和 0.61。在包括 FDA 药物标签数据后，精度、召回率和特异性分别提高到 0.67、0.81 和 0.71。23 个上市后标签更改中有 18 个被正确识别。TAE 分析显示出作为在药物批准时预测不良事件的方法的潜力。
